SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1351–1375 of 3073 papers

TitleStatusHype
Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation—0
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping—0
Amortized Active Learning for Nonparametric Functions—0
Discovering and forecasting extreme events via active learning in neural operators—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models—0
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog—0
Amortized nonmyopic active search via deep imitation learning—0
Active Learning for NLP with Large Language Models—0
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors—0
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images—0
A Multitask Active Learning Framework for Natural Language Understanding—0
Discriminative Active Learning for Domain Adaptation—0
Discriminative Batch Mode Active Learning—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
DroidStar: Callback Typestates for Android Classes—0
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS—0
Distance-Penalized Active Learning Using Quantile Search—0
An Active Learning Based Approach For Effective Video Annotation And Retrieval—0
Distilling the Posterior in Bayesian Neural Networks—0
Distributed Safe Learning and Planning for Multi-robot Systems—0
Dual Adversarial Network for Deep Active Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified